Autonomous vehicles are paving way for a new mode of transportation. Autonomous vehicles require minimum or no intervention from vehicle's driver. Generally, some autonomous vehicles need only an initial input from the driver, whereas some other designs of the autonomous vehicles are continuously under control of the driver. There are some autonomous vehicles that can be remotely controlled. For example, automatic parking in vehicles is an example of the autonomous vehicle in operation.
Autonomous vehicles face dynamic environment that is the environment keeps changing every time. The autonomous vehicles need to keep a track of lane markings, road edges, track road curves, varying surfaces that may be include flat surfaces, winding roads, hilly roads etc. Alongside, the autonomous vehicles also need to keep a check on objects that are both stationary or mobile like a tree or a human or an animal. Hence, the autonomous vehicles need to capture a huge amount of information that keeps on changing every time.
Therefore, to overcome and meet these challenges, autonomous vehicles are provided with various set of sensors. These sensors help the vehicle to gather all around information and help in increasing the degree of autonomy of the vehicle. The various types of sensors currently being used in autonomous vehicles are LiDAR sensors, Ultrasonic sensors, Image sensors, Global Positioning System (GPS) sensors, Inertial Measurement unit (IMU) sensors, dead reckoning sensors, Microbolo sensors, Speed sensors, Steering-angle sensors, Rotational speed sensors, and RADAR sensors. Two of the most used sensors are LiDAR and RADAR sensors.
LiDAR sensors: LiDAR is a device that maps objects in 3-dimensional by bouncing laser beams off its real-world surroundings. LiDAR in automotive systems typically use 905 nm wavelength that can provide up to 200 m range in restricted FOVs (field of views). These sensors scan the environment, around the vehicle, with a non-visible laser beam. LIDAR sensor continually fires off beams of laser light, and then measures how long it takes for the light to return to the sensor. The laser beam generated in of low intensity and non-harmful. The beam visualizes objects and measures ranges to create a 3D image of the vehicle's surrounding environment. LiDAR sensors are very accurate and can gather information to even up to very close distances around the vehicle. However, LiDAR sensors are generally bulky, complex in design and expensive to use. The costs can go up to $100,000. LiDAR may also require complex computing of the data collected that also adds to the costs. Also, LiDARs can only capture data up to a distance of 200 m.
It is to be noted that LiDAR requires optical filters to remove sensitivity to ambient light and to prevent spoofing from other LiDARs. Also, the laser technology used has to be “eye-safe”. Recently mechanical scanning LiDAR, that physically rotate the laser and receiver assembly to collect data over an area that spans up to 360° have been replaced with Solid State LiDAR (SSL) that have no moving parts and are therefore more reliable especially in an automotive environment for long term reliability. However, SSLs currently have lower field-of-view (FOV) coverage.
RADAR sensors: RADAR sensors basically send out electro-magnetic waves. When these waves hit an obstacle, they get reflected. Thus, revealing how far away an object is and how fast is it approaching.
Automotive RADARs can be categorized into three types: long range RADARs, medium range RADARs and short-range RADARs. Long range RADARs are used for measuring the distance to and speed of other vehicles. Medium range RADARs are used for detecting objects within a wider field of view e.g. for cross traffic alert systems. Short range RADARs are used for sensing in the vicinity of the car, e.g. for parking aid or obstacle detection. Depending on the application, RADAR requirements differ. Short range applications require a steerable antenna with a large scanning angle, creating a wide field of view. Long range applications on the other hand, require more directive antennas that provide a higher resolution within a more limited scanning range. Two different frequency bands are mainly used for automotive RADARs: the 24 GHz band and the 77 GHz band. The 77 GHz band offers higher performance, but it is also more challenging to implement since for example losses are much higher at these frequencies. The 24 GHz RADARs are easier to develop but are larger in size, making it difficult to integrate them in a vehicle. RADARs operating at 24 GHz require around three times larger antennas than RADARs operating at 77 GHz, to achieve the same performance. A 77 GHz RADAR would thus be much smaller resulting in easier integration and lower cost. Moving to higher frequencies enables RADARs with a better resolution. However, a major challenge posed is to develop steerable antennas for 77 GHz RADARs with high enough performance at a reasonable cost.
Automotive RADAR systems use a pulse-Doppler approach, where the transmitter operates for a short period, known as the pulse repetition interval (PRI), then the system switches to receive mode until the next transmit pulse. As the RADAR returns, the reflections are processed coherently to extract range and relative motion of detected objects. Another approach is to use continuous wave frequency modulation (CWFM). This approach uses a continuous carrier frequency that varies over time with a receiver on constantly. To prevent the transmit signal from leaking into the receiver, separate transmit and receive antennas are used.
RADAR sensors are low priced and provide as excellent sensors. RADARs also cost very less and may be procured within $150. These sensors work extremely accurately in bad weather conditions like fog, snow, dirt, etc. RADAR sensors use extremely simple circuitry and thus are smaller in size that makes them easy to be manufactured, installed and used. However, one of the major drawbacks of the RADAR sensors is that they give confused results when multiple objects are within the range. They are not able to filter noise in such situations. Existing RADARs do not offer the necessary resolution to distinguish objects with sufficient reliability. One of the main problems faced is the separation of small and large objects that travel at the same distance and velocity in adjacent lanes, e.g. a motorcycle driving in the lane next to a truck.
Major factors affecting RADAR performance are:
Transmitter Power and Antenna Size:
The maximum range of a RADAR system depends in large part on the average power of its transmitter and the physical size of its antenna. This is also called the power-aperture product. There are practical limits to each of these.
Receiver Noise:
The sensitivity of a RADAR receiver is determined by the unavoidable noise that appears at its input. At microwave RADAR frequencies, the noise that limits detectability is usually generated by the receiver itself (i.e., by the random motion of electrons at the input of the receiver) rather than by external noise that enters the receiver via the antenna.
Target Size:
The size of a target as “seen” by RADAR is not always related to physical size of the object. The measure of the target size as observed by RADAR is called RADAR cross section and is determined in units of area (square metres). It is possible for two targets with the same physical cross-sectional area to differ considerably in RADAR size, or RADAR cross section. For example, a flat plate 1 square metre in area will produce a RADAR cross section of about 1,000 square metres at a frequency of 3 GHz when viewed perpendicular to the surface. A cone-sphere (an object resembling an ice-cream cone) when viewed in the direction of the cone rather than the sphere could have a RADAR cross section of about 0.001 square metre even though its projected area is also 1 square metre. Hence, this may cause calculation mistakes and may give wrong estimation of the objects identified.
Clutter:
Echoes form environment factors like land, rain, birds and other similar objects may cause nuisance to detect objects. Clutter makes it difficult to identify objects and their properties to a considerable extent.
Interference:
Signals from nearby RADARs and other transmitters can be strong enough to enter a RADAR receiver and produce spurious responses. Interference is not as easily ignored by automatic detection and tracking systems. Hence, interference may further add to noise to the RADAR signals.
Comparison Between LiDAR and RADAR
As compared to LiDAR sensors, RADAR sensors provide more robust information to the vehicles. LiDAR sensors are generally mounted on top of the vehicle and are mechanically rotated to gather surrounding information. This rotational movement is prone to dysfunction. Whereas in case of RADAR, as they are solid state and have no moving parts hence have minimal rate of failures.
Also, LiDAR sensors produce pulsed laser beams and hence are able to gather information only when the pulsed beam is generating the laser beams. RADAR sensors can generate continuous beams and hence provide continuous information.
Also, LiDAR sensors generate enormous and complex data for which complex computational modules are required to be used. For example, some types of LIDAR systems generate amounts of 1-Gb/s data that require substantial amount of computation by strong computers to process such high mount of data. In some cases, these massive computations require additional computation and correlation of information from other sensors and sources of information. This increases cost heads for vehicle manufacturers. Whereas, RADAR sensors only generate small fractions of data that is easy to compute.
LiDAR sensors are also sensitive to adverse weather conditions such as rain, fog, and snow while RADAR sensors are not prone to any weather conditions.
However, RADAR sensors are challenged when dealing with slow moving objects such as cars, bicycles and pedestrians. Furthermore, these traditional RADAR systems, whether using a modulated or non-modulated signal, have difficulties identifying objects that are very close to each other since one of them will be obscured by the phase noise of the system. Also, the drawback of existing RADAR sensors is the impact on their accuracy due to the phase noise of its frequency source, the synthesizer. RADAR sensors are not able to relay size and shape of objects as accurately as LiDAR. RADAR sensors are not a stand-alone solution. They are accompanied by ultrasonic sensors or cameras.
Therefore, there is a need for an enhanced RADAR system capable of implementing artificial intelligence for helping in making informed decisions based on surrounding information. Furthermore, the system should be capable to overcome the shortcomings of the existing systems and technologies.